Design and development of a machine-learning-driven opioid overdose risk prediction tool integrated in electronic health records in primary care settings.

BACKGROUND: Integrating advanced machine-learning (ML) algorithms into clinical practice is challenging and requires interdisciplinary collaboration to develop transparent, interpretable, and ethically sound clinical decision support (CDS) tools. We aimed to design a ML-driven CDS tool to predict opioid overdose risk and gather feedback for its integration into the University of Florida Health (UFHealth) electronic health record (EHR) system. METHODS: We used user-centered design methods to integrate the ML algorithm into the EHR system. The backend and UI design sub-teams collaborated closely, both informed by user feedback sessions. We conducted seven user feedback sessions with five UF Health primary care physicians (PCPs) to explore aspects of CDS tools, including workflow, risk display, and risk mitigation strategies. After customizing the tool based on PCPs' feedback, we held two rounds of one-on-one usability testing sessions with 8 additional PCPs to gather feedback on prototype alerts. These sessions informed iterative UI design and backend processes, including alert frequency and reappearance circumstances. RESULTS: The backend process development identified needs and requirements from our team, information technology, UFHealth, and PCPs. Thirteen PCPs (male = 62%, White = 85%) participated across 7 user feedback sessions and 8 usability testing sessions. During the user feedback sessions, PCPs (n = 5) identified flaws such as the term "high risk" of overdose potentially leading to unintended consequences (e.g., immediate addiction services referrals), offered suggestions, and expressed trust in the tool. In the first usability testing session, PCPs (n = 4) emphasized the need for natural risk presentation (e.g., 1 in 200) and suggested displaying the alert multiple times yearly for at-risk patients. Another 4 PCPs in the second usability testing session valued the UFHealth-specific alert for managing new or unfamiliar patients, expressed concerns about PCPs' workload when prescribing to high-risk patients, and recommended incorporating the details page into training sessions to enhance usability. CONCLUSIONS: The final backend process for our CDS alert aligns with PCP needs and UFHealth standards. Integrating feedback from PCPs in the early development phase of our ML-driven CDS tool helped identify barriers and facilitators in the CDS integration process. This collaborative approach yielded a refined prototype aimed at minimizing unintended consequences and enhancing usability.

High-Resolution Distance Dependence Interrogation of Scanning Ion Conductance Microscopic Tip-Enhanced Raman Spectroscopy Enabled by Two-Dimensional Molybdenum Disulfide Substrates.

Scanning ion conductance microscopy (SICM) is a powerful surface imaging tool used in the electrolytic environment. Tip-enhanced Raman spectroscopy (TERS) can give more information in addition to the morphology provided by the SICM by utilizing label-free Raman spectroscopy aided by the localized plasmonic enhancement from the metal-coated probes. In this study, the integration of SICM with TERS is demonstrated through employing a silver-coated plasmonic nanopipette. Leveraging a two-dimensional (2D) molybdenum disulfide (MoS2) as a model system, the SICM-TERS enhancement factor was estimated to be ∼105, supported by finite-difference time-domain (FDTD) simulation. Moreover, the subnanometer distance dependence SICM-TERS study reveals the tensile stress and structural changes caused by the nanopipette. These findings illustrate the potential of SICM-TERS for providing comprehensive morphological and chemical insights into electrolytic environments, paving the way for future investigations of electrocatalytic and biological systems.

Two new abietane diterpenoids from Caryopteris trichosphaera.

Two new abietane diterpenoids, cartrisine A (1) and cartrisine B (2) were isolated from Caryopteris trichosphaera. Their structures were elucidated based on HR-ESI-MS and NMR spectral data. Compound 2 exhibited moderate antibacterial bioactivity against MRSA and VRE, and showed a strong synergistic effect with market antibiotics against MRSA and VRE.

Roles of osteocalcin in the central nervous system.

BACKGROUND: Bone-derived protein osteocalcin, which has beneficial effects on brain function, may be a future research direction for neurological disorders. A growing body of evidence suggests a link between osteocalcin and neurological disorders, but the exact relationship is contradictory and unclear. SCOPE OF REVIEW: The aim of this review is to summarize the current research on the interaction between osteocalcin and the central nervous system and to propose some speculative future research directions. MAJOR CONCLUSIONS: In the normal central nervous system, osteocalcin is involved in neuronal structure, neuroprotection, and the regulation of cognition and anxiety. Studies on osteocalcin-related abnormalities in the central nervous system are divided into animal model studies and human studies, depending on the subject. In humans, the link between osteocalcin and brain function is inconsistent. These conflicting data may be due to methodological inconsistencies. By reviewing the related literature on osteocalcin, some comorbidities of the bone and nervous system and future research directions related to osteocalcin are proposed.

Phylogeny, adaptive evolution, and taxonomy of Acronema (Apiaceae): evidence from plastid phylogenomics and morphological data.

Introduction: The genus Acronema, belonging to Apiaceae, includes approximately 25 species distributed in the high-altitude Sino-Himalayan region from E Nepal to SW China. This genus is a taxonomically complex genus with often indistinct species boundaries and problematic generic delimitation with Sinocarum and other close genera, largely due to the varied morphological characteristics. Methods: To explore the phylogenetic relationships and clarify the limits of the genus Acronema and its related genera, we reconstructed a reliable phylogenetic framework with high support and resolution based on two molecular datasets (plastome data and ITS sequences) and performed morphological analyses. Results: Both phylogenetic analyses robustly supported that Acronema was a non-monophyletic group that fell into two clades: Acronema Clade and East-Asia Clade. We also newly sequenced and assembled sixteen Acronema complete plastomes and performed comprehensively comparative analyses for this genus. The comparative results showed that the plastome structure, gene number, GC content, codon bias patterns were high similarity, but varied in borders of SC/IR and we identified six different types of SC/IR border. The SC/IR boundaries of Acronema chienii were significantly different from the other Acronema members which was consistent with the type VI pattern in the genus Tongoloa. We also identified twelve potential DNA barcode regions (ccsA, matK, ndhF, ndhG, psaI, psbI, rpl32, rps15, ycf1, ycf3, psaI-ycf4 and psbM-trnD) for species identification in Acronema. The molecular evolution of Acronema was relatively conservative that only one gene (petG) was found to be under positive selection (ω = 1.02489). Discussion: The gene petG is one of the genes involved in the transmission of photosynthetic electron chains during photosynthesis, which plays a crucial role in the process of photosynthesis in plants. This is also a manifestation of the adaptive evolution of plants in high-altitude areas to the environment. In conclusion, our study provides novel insights into the plastome adaptive evolution, phylogeny, and taxonomy of genus Acronema.

Peering through the hedge: Multiple datasets yield insights into the phylogenetic relationships and incongruences in the tribe Lilieae (Liliaceae).

The increasing use of genome-scale data has significantly facilitated phylogenetic analyses, contributing to the dissection of the underlying evolutionary mechanisms that shape phylogenetic incongruences, such as incomplete lineage sorting (ILS) and hybridization. Lilieae, a prominent member of the Liliaceae family, comprises four genera and approximately 260 species, representing 43% of all species within Liliaceae. They possess high ornamental, medicinal and edible values. Yet, no study has explored the validity of various genome-scale data in phylogenetic analyses within this tribe, nor have potential evolutionary mechanisms underlying its phylogenetic incongruences been investigated. Here, transcriptome, Angiosperms353, plastid and mitochondrial data, were collected from 50 to 93 samples of Lilieae, covering all four recognized genera. Multiple datasets were created and used for phylogenetic analyses based on concatenated and coalescent-based methods. Evolutionary rates of different datasets were calculated, and divergence times were estimated. Various approaches, including coalescence simulation, Quartet Sampling (QS), calculation of concordance factors (gCF and sCF), as well as MSCquartets and reticulate network inference, were carried out to infer the phylogenetic discordances and analyze their underlying mechanisms using a reduced 33-taxon dataset. Despite extensive phylogenetic discordances among gene trees, robust phylogenies were inferred from nuclear and plastid data compared to mitochondrial data, with lower synonymous substitution detected in mitochondrial genes than in nuclear and plastid genes. Significant ILS was detected across the phylogeny of Lilieae, with clear evidence of reticulate evolution identified. Divergence time estimation indicated that most of lineages in Lilieae diverged during a narrow time frame (ranging from 5.0 Ma to 10.0 Ma), consistent with the notion of rapid radiation evolution. Our results suggest that integrating transcriptomic and plastid data can serve as cost-effective and efficient tools for phylogenetic inference and evolutionary analysis within Lilieae, and Angiosperms353 data is also a favorable choice. Mitochondrial data are more suitable for phylogenetic analyses at higher taxonomic levels due to their stronger conservation and lower synonymous substitution rates. Significant phylogenetic incongruences detected in Lilieae were caused by both incomplete lineage sorting (ILS) and reticulate evolution, with hybridization and "ghost introgression" likely prevalent in the evolution of Lilieae species. Our findings provide new insights into the phylogeny of Lilieae, enhancing our understanding of the evolution of species in this tribe.

ER-phagy restrains inflammatory responses through its receptor UBAC2.

ER-phagy, a selective form of autophagic degradation of endoplasmic reticulum (ER) fragments, plays an essential role in governing ER homeostasis. Dysregulation of ER-phagy is associated with the unfolded protein response (UPR), which is a major clue for evoking inflammatory diseases. However, the molecular mechanism underpinning the connection between ER-phagy and disease remains poorly defined. Here, we identified ubiquitin-associated domain-containing protein 2 (UBAC2) as a receptor for ER-phagy, while at the same time being a negative regulator of inflammatory responses. UBAC2 harbors a canonical LC3-interacting region (LIR) in its cytoplasmic domain, which binds to autophagosomal GABARAP. Upon ER-stress or autophagy activation, microtubule affinity-regulating kinase 2 (MARK2) phosphorylates UBAC2 at serine (S) 223, promoting its dimerization. Dimerized UBAC2 interacts more strongly with GABARAP, thus facilitating selective degradation of the ER. Moreover, by affecting ER-phagy, UBAC2 restrains inflammatory responses and acute ulcerative colitis (UC) in mice. Our findings indicate that ER-phagy directed by a MARK2-UBAC2 axis may provide targets for the treatment of inflammatory disease.

Photocatalytic fluoroalkylation by ligand-to-metal charge transfer.

Trifluoromethyl (CF3) and other fluoroalkyl groups are of great significance in the fields of pharmaceutical chemistry and agricultural chemicals. Fluoroalkyl acids, especially trifluoroacetic acid (TFA) is considered the most ideal fluoroalkylation reagent due to its low cost and easy availability. However, the extremely high oxidation potential requirement of TFA limits its wide application. In recent years, since visible-light-induced fluoroalkylation through the ligand-to-metal charge transfer (LMCT) process can overcome the above limitations, it has become an effective synthetic tool for the construction of fluorinated compounds with complex molecules and structures. In this review, according to the classification of different metal catalysts, we summarize the trifluoromethylation and fluoroalkylation of olefins, heteroaromatics, and terminal alkynes in different metal catalytic systems and their corresponding reaction mechanisms. The photocatalytic fluoroalkylation via LMCT is believed to expedite the development of fluoro-containing drugs, and more novel fluoroalkylation methologies using this strategy will be disclosed.

Discovery of new pesticide candidates from nature: design, synthesis and bioactivity research of rutaecarpine derivatives.

BACKGROUND: The invasion of viruses and fungi can cause pathological changes in the normal growth of plants and is an important factor in causing plant infectious diseases. These pathogenic microorganisms can also secrete toxic metabolites, affecting crop quality and posing a threat to human health. In this work, we selected the natural product rutaecarpine as the lead compound to achieve the total synthesis and structural derivation. The antiphytoviral activities of these compounds were systematically studied using tobacco mosaic virus (TMV) as the tested strain, and the structure-activity relationships were summarized. RESULT: The anti TMV activities of compounds 5a, 5n, 6b, and 7c are significantly higher than that of commercial antiviral agent ningnanmycin. We chose 5n for further antiviral mechanism research, and the results showed that it can directly act on viral particles. The molecular docking results further confirmed the interaction of compound 5n and coat protein (CP). These compounds also exhibited broad-spectrum fungicidal activities against eight plant pathogens. Especially compounds 5j and 5p have significant anti-fungal activities (EC50: 5j, 1.76 µg mL-1; 5p, 1.59 µg mL-1) and can be further studied as leads for plant-based anti-fungal agents. CONCLUSION: The natural product rutaecarpine and its derivatives were synthesized, and evaluated for their anti-TMV and fungicidal activities. Compounds 5n and 5p with good activities emerged as new antiviral and anti-fungal candidates, respectively. This study provides important information for the research and development of the novel antiviral and fungicidal agents based on rutaecarpine derivatives. © 2024 Society of Chemical Industry.

Matrix Neurodynamic Approaches for Rank Minimization: Finite/Fixed-Time Convergence Technique.

This article presents two innovative matrix neurodynamic approaches (MNAs) designed to tackle the rank minimization problem. First, by introducing the matrix norm-normalized sign function, two variants of MNA are developed: finite-time converging MNA (FIN t -MNA) and fixed-time converging MNA (FIX t -MNA). Then, the proposed approaches are shown to guarantee the existence and uniqueness of solutions, and based on Lyapunov analysis, it is demonstrated that the proposed approaches converge to the optimal solution within FIN t and FIX t . In addition, upper bounds on the settling time are determined using finite-time and fixed-time lemmas, with subsequent analysis examining the influence of tunable parameters on these bounds for the two approaches through the control variable method. Finally, numerical examples and an image completion experiment confirm the effectiveness and superiority of the proposed approaches compared with the existing MNA and two classical approaches.

G20 roadmap for carbon neutrality: The role of Paris agreement, artificial intelligence, and energy transition in changing geopolitical landscape.

The rapid advancement of artificial intelligence (AI) in the 21st century is driving profound societal changes and playing a crucial role in optimizing energy systems to achieve carbon neutrality. Most G20 nations have developed national AI strategies and are advancing AI applications in energy, manufacturing, and agriculture sectors to meet this goal. However, disparities exist among these nations, creating an "AI divide" that needs to be addressed for regulatory consistency and fair distribution of AI benefits. Here, we look at the linear effects of AI and the Paris Agreement (AI), as well as their potential interaction on carbon neutrality. We also investigate whether geopolitical risk (GPR) can hinder or enhance efforts to attain carbon neutrality through energy transition (ET). To measure carbon neutrality of G20 countries, we employed a robust parametric Malmquist index combined with the fixed-effect panel stochastic frontier model to account for heterogeneity. Results indicate that from 1990 to 2022, carbon neutrality has improved primarily due to technological advancements. Developed G20 countries led in technological progress, while developing countries showed modest gains in carbon efficiency. Using the Driscoll-Kraay robust standard error method, we found that AI has a positive but insignificant linear effect on carbon neutrality. However, the interaction between PA and AI was positive and statistically significant, suggesting that PA augments AI's potential in accelerating carbon neutrality. Energy transition accelerates carbon neutrality in both developed and developing G20 countries. However, the role of energy transition in achieving carbon neutrality becomes negative when the interaction term between energy transition and geopolitical risk (ET × GRP) is incorporated. Regarding control variables, green innovation positively impacts carbon neutrality, whereas financial development has an insignificant effect. Industrial structure and foreign direct investment both negatively affect carbon neutrality, thereby supporting the pollution haven hypothesis. It is recommended that strategies to bridge the "AI divide" and uphold geopolitical stability are crucial to achieve carbon neutrality.

Synthesis of Ultrahigh-Purity (6,5) Carbon Nanotubes Using a Trimetallic Catalyst.

Chirality-controlled synthesis of carbon nanotubes (CNTs) is one of the ultimate goals in the field of nanotube synthesis. At present, direct synthesis achieving a purity of over 90%, which can be called single-chirality synthesis, has been achieved for only two types of chiralities: (14,4) and (12,6) CNTs. Here, we realized an ultrahigh-purity (∼95.8%) synthesis of (6,5) CNTs with a trimetallic catalyst NiSnFe. Partial formation of Ni3Sn crystals was found within the NiSnFe nanoparticles. The activation energy for the selective growth of (6,5) CNTs decreased owing to the formation of Ni3Sn crystals, resulting in the high-purity synthesis of (6,5) CNTs. Transmission electron microscopy (TEM) reveals that one-dimensional (1D) crystals of periodic strip lines with 8.8 Šspacing are formed within the as-grown ultrahigh-purity (6,5) CNTs, which are well-matched with the simulated TEM image of closely packed 37 (6,5) CNTs with 2.8 Šintertube distance, indicating the direct formation of chirality-pure (6,5)-CNT bundle structures. The photoluminescence (PL) lifetime increases more than 20 times by the formation of chirality-pure bundle structures of (6,5) CNTs compared to that of isolated (6,5) CNTs. This can be explained by exciton delocalization or intertube excitons within bundle structures of chirality-pure (6,5) CNTs.

Synthesis of Heptacyclic Compounds through C-H Bond Activation-Initiated Cascade Reactions.

Presented herein is an atom- and step-economical method enabling the precise assembly of a heptacyclic scaffold containing both azocine and indoline units through the cascade reactions of indolin-1-yl(aryl)methanimines with diazo indanediones. The formation of products involves C-H bond activation and double carbene insertion followed by intramolecular condensation, retro-[2 + 2] cycloaddition, and recyclization. This cascade reaction not only provided a concise and straightforward strategy for the synthesis of the otherwise difficult to obtain heptacyclic compounds from readily available substrates but also disclosed an unprecedented reaction mode for indoline derivatives and diazo compounds. In general, this novel synthetic protocol has advantages, such as easily obtainable substrates, structurally sophisticated products, concise synthetic procedure, good compatibility with diverse functional groups, and ready scalability. Moreover, the products thus obtained showed decent antiproliferative activity against three human cancer cell lines.

A Fixed-Time Proximal Gradient Neurodynamic Network With Time-Varying Coefficients for Composite Optimization Problems and Sparse Optimization Problems With Log-Sum Function.

This article presents a novel proximal gradient neurodynamic network (PGNN) for solving composite optimization problems (COPs). The proposed PGNN with time-varying coefficients can be flexibly chosen to accelerate the network convergence. Based on PGNN and sliding mode control technique, the proposed time-varying fixed-time proximal gradient neurodynamic network (TVFxPGNN) has fixed-time stability and a settling time independent of the initial value. It is further shown that fixed-time convergence can be achieved by relaxing the strict convexity condition via the Polyak-Lojasiewicz condition. In addition, the proposed TVFxPGNN is being applied to solve the sparse optimization problems with the log-sum function. Furthermore, the field-programmable gate array (FPGA) circuit framework for time-varying fixed-time PGNN is implemented, and the practicality of the proposed FPGA circuit is verified through an example simulation in Vivado 2019.1. Simulation and signal recovery experimental results demonstrate the effectiveness and superiority of the proposed PGNN.

New resorcylic acid derivatives of Lysimachia tengyuehensis against MRSA and VRE by interfering with bacterial metabolic imbalance.

The abuse of antibiotics leads to the rapid spread of bacterial resistance, which seriously threatens human life and health. Now, 8 resorcylic acid derivatives, including 4 new compounds (1-4) were isolated from Lysimachia tengyuehensis by bio-guided isolation, and they inhibited both methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus (VRE) (MIC = 4-8 µg/mL). Notably, 1 and 2 rapidly killed MRSA and VRE within 40 min without drug resistance in 20 days. Mechanically, they potently disrupted biofilm and cell membrane by interfering with bacterial metabolic imbalance. The structure-activity relationship (SAR) revealed that the lipophilic long carbon chains (C-5/C-6) and hydrophilic hydroxyl/carboxyl groups were essential for the anti-MRSA and VRE bioactivity. Additionally, they effectively recovered MRSA-infected skin wounds and VRE-infected peritoneal in vivo. Resorcylic acid derivatives showed significant anti-MRSA and VRE bioactivity in vitro and in vivo with potential application for the first time.

Extracting Pulmonary Nodules and Nodule Characteristics from Radiology Reports of Lung Cancer Screening Patients Using Transformer Models.

 Pulmonary nodules and nodule characteristics are important indicators of lung nodule malignancy. However, nodule information is often documented as free text in clinical narratives such as radiology reports in electronic health record systems. Natural language processing (NLP) is the key technology to extract and standardize patient information from radiology reports into structured data elements. This study aimed to develop an NLP system using state-of-the-art transformer models to extract pulmonary nodules and associated nodule characteristics from radiology reports. We identified a cohort of 3080 patients who underwent LDCT at the University of Florida health system and collected their radiology reports. We manually annotated 394 reports as the gold standard. We explored eight pretrained transformer models from three transformer architectures including bidirectional encoder representations from transformers (BERT), robustly optimized BERT approach (RoBERTa), and A Lite BERT (ALBERT), for clinical concept extraction, relation identification, and negation detection. We examined general transformer models pretrained using general English corpora, transformer models fine-tuned using a clinical corpus, and a large clinical transformer model, GatorTron, which was trained from scratch using 90 billion words of clinical text. We compared transformer models with two baseline models including a recurrent neural network implemented using bidirectional long short-term memory with a conditional random fields layer and support vector machines. RoBERTa-mimic achieved the best F1-score of 0.9279 for nodule concept and nodule characteristics extraction. ALBERT-base and GatorTron achieved the best F1-score of 0.9737 in linking nodule characteristics to pulmonary nodules. Seven out of eight transformers achieved the best F1-score of 1.0000 for negation detection. Our end-to-end system achieved an overall F1-score of 0.8869. This study demonstrated the advantage of state-of-the-art transformer models for pulmonary nodule information extraction from radiology reports. Supplementary Information: The online version contains supplementary material available at 10.1007/s41666-024-00166-5.

Develop and validate a computable phenotype for the identification of Alzheimer's disease patients using electronic health record data.

INTRODUCTION: Alzheimer's disease (AD) is often misclassified in electronic health records (EHRs) when relying solely on diagnosis codes. This study aimed to develop a more accurate, computable phenotype (CP) for identifying AD patients using structured and unstructured EHR data. METHODS: We used EHRs from the University of Florida Health (UFHealth) system and created rule-based CPs iteratively through manual chart reviews. The CPs were then validated using data from the University of Texas Health Science Center at Houston (UTHealth) and the University of Minnesota (UMN). RESULTS: Our best-performing CP was "patient has at least 2 AD diagnoses and AD-related keywords in AD encounters," with an F1-score of 0.817 at UF, 0.961 at UTHealth, and 0.623 at UMN, respectively. DISCUSSION: We developed and validated rule-based CPs for AD identification with good performance, which will be crucial for studies that aim to use real-world data like EHRs. Highlights: Developed a computable phenotype (CP) to identify Alzheimer's disease (AD) patients using EHR data.Utilized both structured and unstructured EHR data to enhance CP accuracy.Achieved a high F1-score of 0.817 at UFHealth, and 0.961 and 0.623 at UTHealth and UMN.Validated the CP across different demographics, ensuring robustness and fairness.

κ-Carrageenan masking bitterness perception in surimi gels containing potassium chloride-based salt substitutes: Gel properties, oral processing, and sensory evaluation.

κ-Carrageenan (CG) was employed to mask the bitterness induced by 50% KCl in surimi gels to achieve salt reduction and gel performance improvement. The combination of KCl and CG (KCl + CG) yielded the increased textural characteristics and water-holding capacity (WHC) of surimi gels and facilitated the transition of free water to immobilized water. In addition, the KCl + CG supplement increased the turbidity and particle size of myofibrillar protein (MP) sols but decreased the surface hydrophobicity in a dose-dependent manner. The hydrophobic interactions and disulfide bonds played crucial roles in maintaining the stability of MP gels. The specific binding of potassium ions to the sulfate groups of CG limited the release and diffusion of potassium ions from the surimi gels during oral processing, effectively masking the bitterness perception and maintaining the saltiness perception. This study provides a promising strategy to reduce the utilization of sodium salt in surimi products.

The Concentration of Benzo[a]pyrene in Food Cooked by Air Fryer and Oven: A Comparison Study.

The air fryer utilizes heated air rather than hot oil to achieve frying, eliminating the need for cooking oil, rendering it a healthier cooking method than traditional frying and baking. However, there is limited evidence supporting that the air fryer could effectively reduce the level of food-derived carcinogen. In this study, we compared the concentration of Benzo[a]pyrene (BaP), a typical carcinogen, in beef patties cooked using an air fryer and an oven, under different cooking conditions, including temperatures (140 °C, 160 °C, 180 °C, and 200 °C), times (9, 14, and 19 min), and oil added or not. The adjusted linear regression analysis revealed that the BaP concentration in beef cooked in the air fryer was 22.667 (95% CI: 15.984, 29.349) ng/kg lower than that in beef cooked in the oven. Regarding the air fryer, the BaP concentration in beef cooked without oil brushing was below the detection limit, and it was significantly lower than in beef cooked with oil brushing (p < 0.001). Therefore, cooking beef in the air fryer can effectively reduce BaP concentration, particularly due to the advantage of oil-free cooking, suggesting that the air fryer represents a superior option for individuals preparing meat at high temperatures.

The effect of modified Qiyuan paste on mice with low immunity and sleep deprivation by regulating GABA nerve and immune system.

BACKGROUND: Low immunity and sleep disorders are prevalent suboptimal health conditions in contemporary populations, which render them susceptible to the infiltration of pathogenic factors. LJC, which has a long history in traditional Chinese medicine for nourishing the Yin and blood and calming the mind, is obtained by modifying Qiyuan paste. Dendrobium officinale Kimura et Migo has been shown to improve the immune function in sleep-deprived mice. In this study, based on the traditional Chinese medicine theory, LJC was prepared by adding D. officinale Kimura et Migo to Qiyuan paste decoction. METHODS: Indicators of Yin deficiency syndrome, such as back temperature and grip strength, were measured in each group of mice; furthermore, behavioral tests and pentobarbital sodium-induced sleep tests were performed. An automatic biochemical analyzer, enzyme-linked immunosorbent assay kit, and other methods were used to determine routine blood parameters, serum immunoglobulin (IgG, IgA, and IgM), cont (C3, C4), acid phosphatase (ACP) and lactate dehydrogenase (LDH) levels in the spleen, serum hemolysin, and delayed-type hypersensitivity (DTH) levels. In addition, serum levels of γ-aminobutyric acid (GABA) and glutamate (Glu) were detected using high-performance liquid chromatography (HPLC). Hematoxylin-eosin staining and Nissl staining were used to assess the histological alterations in the hypothalamus tissue. Western blot and immunohistochemistry were used to detect the expressions of the GABA pathway proteins GABRA1, GAD, GAT1, and GABAT1 and those of CD4+ and CD8+ proteins in the thymus and spleen tissues. RESULTS: The findings indicated that LJC prolonged the sleep duration, improved the pathological changes in the hippocampus, effectively upregulated the GABA content in the serum of mice, downregulated the Glu content and Glu/GABA ratio, enhanced the expressions of GABRA1, GAT1, and GAD, and decreased the expression of GABAT1 to assuage sleep disorders. Importantly, LJC alleviated the damage to the thymus and spleen tissues in the model mice and enhanced the activities of ACP and LDH in the spleen of the immunocompromised mice. Moreover, serum hemolysin levels and serum IgG, IgA, and IgM levels increased after LJC administration, which manifested as increased CD4+ content, decreased CD8+ content, and enhanced DTH response. In addition, LJC significantly increased the levels of complement C3 and C4, increased the number of white blood cells and lymphocytes, and decreased the percentage of neutrophils in the blood. CONCLUSIONS: LJC can lead to improvements in immunocompromised mice models with insufficient sleep. The underlying mechanism may involve regulation of the GABA/Glu content and the expression levels of GABA metabolism pathway-related proteins in the brain of mice, enhancing their specific and nonspecific immune functions.